Respondent driven sampling (RDS) is a recruitment method for hard-to-sample populations that are rare in number and/or elusive due to highly-stigmatized or illicit behaviors. For these groups, traditional probability sampling rarely offers feasibility, because it requires prohibitively high screening costs to locate eligible persons, and, even when eligible persons are located, their desire to hide produces false negatives. Based on the premise that people of similar traits form some type of social networks, RDS exploits the existing networks for recruitment and has been applied to numerous studies. What sets RDS apart from traditional sampling is that the recruitment process is mostly controlled by participants themselves through their chain- referral that asks participants to recruit other eligible persons from their networks. The use of organic social networks for sampling is an innovative feature of RDS. This, however, comes with one major challenge. In order to capitalize on RDS, participants need to cooperate with recruitment requests. Because of noncooperation, the sample may stop growing in size, resulting in a project overrun. However, the lack of attention to this noncooperation process in the literature makes RDS data collection progress extremely difficult to predict at the design stage, and when faces with undesirable (and often unexpected) challenges, researchers are forced to make unplanned design changes (e.g., offering larger incentives) on the spur of the moment in hopes of making RDS ?work?. Additionally, noncooperation leads to a violation of a critical assumption of RDS inferences. In sum, the current practice of RDS lacks operational and statistical reproducibility, making its scientific integrity questionable. This study attempts to improve reproducibility of RDS by proposing Adaptive-RDS (A-RDS) as a design framework and to provide practical tools on which researchers rely for successful implementation of RDS and by developing A-RDS specific design guidelines and software that will allow monitoring RDS data collection progress and improve inferences that closely mirror the true data generation process. Under A-RDS, we will plan design adaptation strategies, including indicators and rules for adaptations prior to the data collection. During the field work, instead of assuming the same recruitment cooperation patterns across participants, we will predict individual-level cooperation propensities from incoming data and tailor the number and type of coupons for each participant received based on the pre-specified rules. For doing so, data collection progress will be closely monitored and used for making adaptation decisions. In particular, this approach is empirically applied to PWID studies to provide data for addressing rapidly escalated issues with opioid use. By providing a practical yet data-driven, rule-based tool to the research community, the proposed study will boost researchers' control on the operations of RDS, leading to not only improved reproducibility but also increased chances of meeting critical assumptions in RDS required for valid inferences.